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arXiv 2022年

作者： Wang, Kunlun Jin, Jiong Yang, Yang Zhang, Tao Nallanathan, Arumugam Tellambura, Chintha Jabbari, Bijan The Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China The School of Communication and Electronic Engineering East China Normal University Shanghai200241 China The School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia Terminus Group Beijing100027 China ShanghaiTech University Shanghai201210 China Peng Cheng Laboratory Shenzhen518055 China United States The School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom The Department of Electrical and Computer Engineering University of Alberta Canada The Department of Electrical and Computer Engineering George Mason University FairfaxVA United States

With the development of next-generation wireless networks, the Internet of Things (IoT) is evolving towards the intelligent IoT (iIoT), where intelligent applications usually have stringent delay and jitter requirements. In order to provide low-latency services to heterogeneous users in the emerging iIoT, multi-tier computing was proposed by effectively combining edge computing and fog computing. More specifically, multi-tier computing systems compensate for cloud computing through task offloading and dispersing computing tasks to multi-tier nodes along the continuum from the cloud to things. In this paper, we investigate key techniques and directions for wireless communications and resource allocation approaches to enable task offloading in multi-tier computing systems. A multi-tier computing model, with its main functionality and optimization methods, is presented in details. We hope that this paper will serve as a valuable reference and guide to the theoretical, algorithmic, and systematic opportunities of multi-tier computing towards next-generation wireless networks. Copyright © 2022, The Authors. All rights reserved.

关键词： Resource allocation

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Multi-Slot Distributed Measurement Selection: A Sparsity Learning Approach

Multi-Slot Distributed Measurement Selection: A Sparsity Lea...

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作者： Xia, Qian Wang, Wei Ran, Rong Gong, Yi Zhang, Zhaoyang College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Zhejiang University Hangzhou310027 China School of Electrical and Computer Engineering Ajou University Suwon16499 Korea Republic of Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China

When only a part of measurements are collected sequentially in large-scale Internet of Things (IoT) networks, it is motivated to extract significant information from the collected data for utilizing the communication resources over incoming slots more efficiently. In this article, we study the successive measurement selection problem for sparse parameter estimation and its distributed implementation in large-scale networks. Specifically, we transform the intractable multi-slot measurement selection problem into per-slot subproblems, and in each slot we select the uncollected measurements that are more 'useful' for parameter estimation based on sparse Bayesian learning. In order to deal with the combinatorial problem with distributed implementation, we relax the subproblem and propose an enhanced subgradient method to solve the Boolean constraint. The proposed distributed scheme needs only a common information broadcast by the centralized controller, where the communication overheads only depend on the number of sparse active sources rather than the number of sensors which is always huge. Simulation results demonstrate the performance gain of our proposed measurement selection method compared to the conventional schemes. When the total communication overheads are limited with the network scale, our scheme presents extreme practicability and applicability for the large-scale networks. © 2015 IEEE.

关键词： Wireless sensor networks

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Recombination of localized quasiparticles in disordered superconductors

arXiv

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arXiv 2024年

作者： de Rooij, Steven A.H. Fermin, Remko Kouwenhoven, Kevin Coppens, Tonny Murugesan, Vignesh Thoen, David J. Aarts, Jan Baselmans, Jochem J.A. de Visser, Pieter J. SRON - Netherlands Institute for Space Research Niels Bohrweg 4 CA Leiden2333 Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands Department of Materials Science and Metallurgy University of Cambridge 27 Charles Babbage Rd CambridgeCB3 0FS United Kingdom Huygens-Kamerlingh Onnes Laboratory Leiden University Leiden2300 RA Netherlands

Disordered superconductors offer new impedance regimes for quantum circuits, enable a pathway to protected qubits and improve superconducting single photon detectors due to their high kinetic inductance and sheet resistance. However, the relaxation of quasiparticles, which cause decoherence and microwave loss, is drastically affected by disorder, which is still not understood. We address this issue by measuring the quasiparticle relaxation time in a disordered β-Ta film, which we pattern as the inductor of a microwave resonator. We observe that the quasiparticle relaxation time is governed by the phonon scattering time and is independent on the phononic environment, which is in stark contrast to the relaxation of quasiparticles in ordered superconductors. We explain these observations in terms of the recombination of localized quasiparticles that first delocalize via phonon scattering. We analyse quasiparticle relaxation measurements on superconductors with different degrees of disorder and conclude that this phenomenon is inherent to disordered superconductors. © 2024, CC BY.

关键词： Microwave resonators

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Photonic indistinguishability of the tin-vacancy center in nanostructured diamond

arXiv

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arXiv 2022年

作者： Martínez, Jesús Arjona Parker, Ryan A. Chen, Kevin C. Purser, Carola M. Li, Linsen Michaels, Cathryn P. Stramma, Alexander M. Debroux, Romain Harris, Isaac B. Appel, Martin Hayhurst Nichols, Eleanor C. Trusheim, Matthew E. Gangloff, Dorian A. Englund, Dirk Atatüre, Mete Cavendish Laboratory University of Cambridge JJ Thomson Avenue CambridgeCB3 0HE United Kingdom Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Engineering Science University of Oxford Parks Road OxfordOX1 3PJ United Kingdom

Tin-vacancy centers in diamond are promising spin-photon interfaces owing to their high quantum-efficiency, large Debye-Waller factor, and compatibility with photonic nanostructuring. Benchmarking their single-photon indistinguishability is a key challenge for future applications. Here, we report the generation of single photons with 99.7+0.3−2.5% purity and 63(9)% indistinguishability from a resonantly excited tin-vacancy center in a single-mode waveguide. We obtain quantum control of the optical transition with 1.71(1)-ns-long π-pulses of 77.1(8)% fidelity. A modest Purcell enhancement factor of 12 would enhance the indistinguishability to 95%. The greater than 100 ms spectral stability shown would then enable strings of up to 106 identical photons to be generated. © 2022, CC BY.

关键词： Benchmarking

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Recent advances in photonics of threedimensional Dirac semimetal Cd_(3)As_(2)

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Advanced Photonics Nexus 2022年第2期1卷 1-19页

作者： Renlong Zhou Kaleem Ullah Naveed Hussain Mohammed M.Fadhali Sa Yang Qiawu Lin Muhammad Zubair Muhammad Faisal Iqbal Guangdong University of Education School of Physics and Information EngineeringGuangzhouChina University of Delaware Department of Electrical and Computer EngineeringNewarkDelawareUnited States University of California Department of Electrical Engineering and Computer ScienceIrvineCaliforniaUnited States Jazan University Faculty of ScienceDepartment of PhysicsJazanSaudi Arabia Ibb University Faculty of ScienceDepartment of PhysicsIbbYemen Institute of Energy and Climate Research Materials Synthesis and Processing(IEK-1)Forschungszentrum Jülich GmbHJülichGermany University of Science and Technology of China Hefei National Laboratory for Physical Sciences at the MicroscaleHefeiChina Riphah International University Faisalabad Department of PhysicsFaisalabadPakistan

Due to their unusual features in condensed matter physics and their applicability in optical and optoelectronic applications,three-dimensional Dirac semimetals(3D DSMs)have garnered substantial interest in recent *** contrast to monolayer graphene,3D DSM exhibits linear band dispersion despite its macroscopic ***,being a bulk material,it is easy to make nanostructures with 3D DSM,just as one normally does with metals such as gold and *** 3D DSMs,cadmium arsenide(Cd_(3)As_(2))is quite famous and considered an excellent 3D DSM due to its chemical stability in air and extraordinary optical *** this review,advances in 3D DSM Cd_(3)As_(2)fabrication techniques and recent progress in the photonics of 3D DSM Cd_(3)As_(2)are given and briefly *** photonic features,including linear and nonlinear plasmonics,optical absorption,optical harmonic generation,and ultrafast dynamics,have been explored in *** is expected that Cd_(3)As_(2)would share an excellent tunable photonic response like *** envision that this article may serve as a concise overview of the recent progress of photonics in 3D DSM Cd_(3)As_(2)and provides a compact reference for young researchers.

关键词： Three-dimensional Dirac semimetal Cd_(3)As_(2) photonics tunable nanostructures

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Evaluation of Robustness Metrics for Defense of Machine Learning Systems

Evaluation of Robustness Metrics for Defense of Machine Lear...

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International Conference on Military Communications and Information Systems (ICMCIS)

作者： J. DeMarchi R. Rijken J. Melrose B. Madahar G. Fumera F. Roli E. Ledda M. Aktaş F. Kurth P. Baggenstoss B. Pelzer L. Kanestad Collaborative Engineering Systems & Aerospace Systems Information Supremacy Royal Netherlands Aerospace Centre NLR Amsterdam NLD Cyber & Information Systems Division Defence Science and Technology Laboratory Portondown GBR Department of Electrical and Electronic Engineering University of Cagliari Cagliari ITA Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa ITA Department of Computer Control and Management Engineering Sapienza University of Rome Rome ITA Defence Systems Technologies Division ASELSAN Ankara TUR Fraunhofer Institute for Communication Information Processing and Ergonomics Bonn DEU Swedish Defence Research Agency Cyber Defence and C2 Technology Division Stockholm SWE

In this paper we explore some of the potential applications of robustness criteria for machine learning (ML) systems by way of tangible “demonstrator” scenarios. In each demonstrator, ML robustness metrics are applied to real-world scenarios with military relevance, indicating how they might be used to help detect and handle possible adversarial attacks on ML systems. We conclude by sketching promising future avenues of research in order to: (1) help establish useful verification methodologies to facilitate ML robustness compliance assessment; (2) support development of ML accountability mechanisms; and (3) reliably detect, repel, and mitigate adversarial attack.

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Wireless-Friendly Window Position Optimization for RIS-Aided Outdoor-to-Indoor Networks based on Multi-Modal Large Language Model

arXiv

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arXiv 2024年

作者： Hou, Jinbo Qiu, Kehai Zhang, Zitian Yu, Yong Wang, Kezhi Capolongo, Stefano Zhang, Jiliang Li, Zeyang Zhang, Jie The Department of Electronic and Electrical Engineering the University of Sheffield Sheffield United Kingdom The Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FD United Kingdom Brunel University London United Kingdom The School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China The Design & Health Lab Department of Architecture Built Environment and Construction Engineering Politecnico di Milano Italy The Department of Computer Science Brunel University London Middlesex UxbridgeUB8 3PH United Kingdom The State Key Laboratory of Synthetical Automation for Process Industries The College of Information Science and Engineering Northeastern University Shenyang China The Centre for Wireless Innovation Queen’s University of Belfast BelfastBT3 9DT United Kingdom

This paper aims to simultaneously optimize indoor wireless and daylight performance by adjusting the positions of windows and the beam directions of window-deployed reconfigurable intelligent surfaces (RISs) for RIS-aided outdoor-to-indoor (O2I) networks utilizing large language models (LLM) as optimizers. Firstly, we illustrate the wireless and daylight system models of RIS-aided O2I networks and formulate a joint optimization problem to enhance both wireless traffic sum rate and daylight illumination performance. Then, we present a multi-modal LLM-based window optimization (LMWO) framework, accompanied by a prompt construction template to optimize the overall performance in a zero-shot fashion, functioning as both an architect and a wireless network planner. Finally, we analyze the optimization performance of the LMWO framework and the impact of the number of windows, room size, number of RIS units, and daylight factor. Numerical results demonstrate that our proposed LMWO framework can achieve outstanding optimization performance in terms of initial performance, convergence speed, final outcomes, and time complexity, compared with classic optimization methods. The building’s wireless performance can be significantly enhanced while ensuring indoor daylight performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Daylighting

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120 GOPS Photonic Tensor Core in Thin-film Lithium Niobate for Inference and in-situ Training

arXiv

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arXiv 2023年

作者： Lin, Zhongjin Shastri, Bhavin J. Yu, Shangxuan Song, Jingxiang Zhu, Yuntao Safarnejadian, Arman Cai, Wangning Lin, Yanmei Ke, Wei Hammood, Mustafa Wang, Tianye Xu, Mengyue Zheng, Zibo Al-Qadasi, Mohammed Esmaeeli, Omid Rahim, Mohamed Pakulski, Grzegorz Schmid, Jens Barrios, Pedro Jiang, Weihong Morison, Hugh Mitchell, Matthew Guan, Xun Jaeger, Nicolas A.F. Rusch, Leslie A. Shekhar, Sudip Shi, Wei Yu, Siyuan Cai, Xinlun Chrostowski, Lukas Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada State Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information Technology Sun Yat-sen University Guangdong Guangzhou510275 China Department of Physics Engineering Physics and Astronomy Queen’s University KingstonONK7L 3N6 Canada Department of Electrical and Computer Engineering Université Laval Québec CityQCG1V 0A6 Canada Advanced Electronics and Photonics Research Centre National Research Council OttawaONK1A 0R6 Canada Tsinghua-Berkeley Shenzhen Institute Tsinghua University Shenzhen581055 China

Photonics offers a transformative approach to artificial intelligence (AI) and neuromorphic computing by enabling low-latency, high-speed, and energy-efficient computations. However, conventional photonic tensor cores face significant challenges in constructing large-scale photonic neuromorphic networks. Here, we propose a fully integrated photonic tensor core, consisting of only two thin-film lithium niobate (TFLN) modulators, a III-V laser, and a charge-integration photoreceiver. Despite its simple architecture, it is capable of implementing an entire layer of a neural network with a computational speed of 120 GOPS, while also allowing flexible adjustment of the number of inputs (fan-in) and outputs (fan-out). Our tensor core supports rapid in-situ training with a weight update speed of 60 GHz. Furthermore, it successfully classifies (supervised learning) and clusters (unsupervised learning) 112 × 112-pixel images through in-situ training. To enable in-situ training for clustering AI tasks, we offer a solution for performing multiplications between two negative *** Codes 78A05 Copyright © 2023, The Authors. All rights reserved.

关键词： Tensors

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Fault Diagnosis of Wind Energy Conversion Systems Using Gaussian Process Regression-based Multi-Class Random Forest

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IFAC-PapersOnLine 2022年第6期55卷 127-132页

作者： Majdi Mansouri Radhia Fezai Mohamed Trabelsi Hajji Mansour Hazem Nounou Mohamed Nounou Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Department of Mathematics and Sciences Prince Sultan University Riyadh 11586 Saudi Arabia Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir 5019 Tunisia Electronic and Communications Engineering Department Kuwait College of Science and Technology P. O. Box 27235 Kuwait Higher Institute of Applied Science and Technology of Kasserine University of Kairouan PO Box 471 1200 Kasserine Tunisia Chemical Engineering Program Texas A&M University at Qatar Doha Qatar

This work proposes a new fault diagnosis approach for a wind energy conversion (WEC) system. The proposed technique merges the benefits of feature extraction based on Gaussian Process Regression (GPR) and Multi-Class Random Forest (MCRF)-based fault classification where instances are classified into one or more classes. In the developed GPR-MCRF approach, the nonlinear statistical features including the mean vector M GPR and the variance matrix C GPR are computed using the GPR model with aim of extracting the most relevant features from the WEC system. Then, these features are introduced to the RF classifier for classification and diagnosis purposes. Therefore, the application of the GPR-MCRF technique for WEC systems aims to enhance the use of the classical raw data-based MCRF and diagnosis accuracy. Three kinds of faults (wear-out, open-circuit, and short-circuit faults) are considered in this work. Different case studies are investigated in order to illustrate the effectiveness and robustness of the developed technique compared to the state-of-the-art methods. The obtained results show that the the developed GPR-MCRF technique is an effective feature extraction and fault diagnosis technique for WEC systems.

关键词： Random Forest (RF) Multi-Class Gaussian Process Regression (GPR) Fault Diagnosis Wind Energy Conversion Systems

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Correction to: Multi-level context-driven interaction modeling for human future trajectory prediction

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Neural Computing and Applications 2023年第27期35卷 20441-20441页

作者： He, Zhiquan Sun, Hao Cao, Wenming He, Henry Z. Guangdong Multimedia Information Service Engineering Technology Research Center Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Video Processing and Communication Laboratory Department of Electrical and Computer Engineering University of Missouri Columbia USA

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